Publications by authors named "Roger D Chamberlain"

3 Publications

  • Page 1 of 1

Acceleration of Ungapped Extension in Mercury BLAST.

Microprocess Microsyst 2009 Jun;33(4):281-289

Department of Computer Science and Engineering, Washington University in St. Louis, E-mail:

The amount of biosequence data being produced each year is growing exponentially. Extracting useful information from this massive amount of data efficiently is becoming an increasingly difficult task. There are many available software tools that molecular biologists use for comparing genomic data. This paper focuses on accelerating the most widely used such tool, BLAST. Mercury BLAST takes a streaming approach to the BLAST computation by off loading the performance-critical sections to specialized hardware. This hardware is then used in combination with the processor of the host system to deliver BLAST results in a fraction of the time of the general-purpose processor alone.This paper presents the design of the ungapped extension stage of Mercury BLAST. The architecture of the ungapped extension stage is described along with the context of this stage within the Mercury BLAST system. The design is compact and runs at 100 MHz on available FPGAs, making it an effective and powerful component for accelerating biosequence comparisons. The performance of this stage is 25× that of the standard software distribution, yielding close to 50× performance improvement on the complete BLAST application. The sensitivity is essentially equivalent to that of the standard distribution.
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http://dx.doi.org/10.1016/j.micpro.2009.02.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2771927PMC
June 2009

Mercury BLASTP: Accelerating Protein Sequence Alignment.

ACM Trans Reconfigurable Technol Syst 2008 Jun;1(2)

Dept. of Computer Science and Engineering, Washington University in St. Louis.

Large-scale protein sequence comparison is an important but compute-intensive task in molecular biology. BLASTP is the most popular tool for comparative analysis of protein sequences. In recent years, an exponential increase in the size of protein sequence databases has required either exponentially more running time or a cluster of machines to keep pace. To address this problem, we have designed and built a high-performance FPGA-accelerated version of BLASTP, Mercury BLASTP. In this paper, we describe the architecture of the portions of the application that are accelerated in the FPGA, and we also describe the integration of these FPGA-accelerated portions with the existing BLASTP software. We have implemented Mercury BLASTP on a commodity workstation with two Xilinx Virtex-II 6000 FPGAs. We show that the new design runs 11-15 times faster than software BLASTP on a modern CPU while delivering close to 99% identical results.
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http://dx.doi.org/10.1145/1371579.1371581DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2615407PMC
June 2008

Reliable real-time clinical monitoring using sensor network technology.

AMIA Annu Symp Proc 2009 Nov 14;2009:103-7. Epub 2009 Nov 14.

Washington University in St. Louis, St. Louis, MO, USA.

We propose wireless sensor networks composed of nodes using low-power 802.15.4 radios as an enabling technology for patient monitoring in general hospital wards. A key challenge for such applications is to reliably deliver sensor data from mobile patients. We propose a monitoring system with two types of nodes: patient nodes equipped with wireless pulse oximeters and relays nodes used to route data to a base station. A reliability analysis of data collection from mobile users shows that mobility leads to packet losses exceeding 30%. The majority of packet losses occur between the mobile subjects and the first-hop relays. Based on this insight we developed the Dynamic Relay Association Protocol (DRAP), an effective mechanism for discovering the right relays for patient nodes. DRAP enables highly reliable data collection from mobile subjects. Empirical evaluation showed that DRAP delivered at least 96% of data from multiple users. Our results demonstrate the feasibility of wireless sensor networks for real-time clinical monitoring.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815421PMC
November 2009
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